The problem of features fusion is challenging in multimodal irregular time-series data processing[1]}. For example, for clinical data, patient’s electronic health records can be abstracted into thousands of interrelated medical events with temporal information, including complex allergy history, family genetic history, drug list, hospitalization records and other historical records. Different event has almost absolutely different frequency of recording. E.g. patient’s hospitalization records may be only once a few years, but medication records could be many times a year. Not only different events have different recording frequencies, but also the same type events have significant differences in their different nature. For example, attributes of drug taking events such as drug type, dose and test events include specific indicators and comparison results with normal range values. In order to integrate the features of these events, we must describe these dependencies.
